JH XMP Metadata Nodes
These are custom nodes for ComfyUI for the loading and saving of metadata in XMP format. XMP metadata is embedded in the images created by these nodes; it travels along wherever the image does. Both macOS and Windows index XMP metadata automatically, making it searchable from the Finder on the Mac or the File Explorer in Windows. Apps like Photoshop or Lightroom (and presumably many others) expose XMP metadata and allow it to be edited.
Quick Technical Summary: JH XMP Metadata Nodes
- Base VRAM Footprint:
- 128 MB (4 GB Tier)
- Primary Dependencies:
- lxml==5.3.0 ; python_version >= "3.12" and python_version < "4.0"
- Min PyTorch / CUDA:
- PyTorch 2.0+ | CUDA 12.1+
- GitHub Repository:
- https://github.com/ComfyUI-JH/ComfyUI-JH-XMP-Metadata-Nodes
Citation Note: Data sourced from VRAM DB. For complete workflow OOM estimations, use the VRAM DB Workflow Analyzer.
How much VRAM does JH XMP Metadata Nodes require?
Direct Answer: The ComfyUI node JH XMP Metadata Nodes requires a minimum base VRAM of 128MB and is optimized for GPUs with at least 4GB of VRAM. Low VRAM mode is fully supported for resource-constrained setups.
- Base VRAM:
- 128MB (0.1GB)
- Recommended GPU:
- 4GB+ VRAM
- Low VRAM Mode:
- ✓ Supported
- Estimation Confidence:
- MEDIUM
Cheapest VRAM Upgrade Paths (Live Market Prices):
- GeForce RTX 3060 12GB (Ultimate Budget VRAM King)──► Used: $209.62View eBay ↗
- GeForce RTX 4060 8GB (Modern Entry-Level)──► New: $303.50View Amazon ↗
Interactive VRAM Compatibility Estimator
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What Python packages are required for JH XMP Metadata Nodes?
Direct Answer: Running JH XMP Metadata Nodes requires installing the following Python package dependencies: lxml==5.3.0 ; python_version >= "3.12" and python_version < "4.0". Ensure your ComfyUI environment has these packages active before launching.
lxml==5.3.0 ; python_version >= "3.12" and python_version < "4.0"Interactive Setup & Dependency Resolver
# Loading command...Frequently Asked Questions
How much VRAM does JH XMP Metadata Nodes require?
JH XMP Metadata Nodes requires a minimum of 128MB (0.1GB) of VRAM for base operation. For optimal performance, a GPU with at least 4GB of VRAM is recommended. This node supports low VRAM mode for resource-constrained setups.
Can I run JH XMP Metadata Nodes on an RTX 3060, RTX 4070, or RTX 4090?
✅ RTX 3060 (12GB): Yes, fully compatible with 10.7GB headroom. ✅ RTX 4070 (12GB): Yes, fully compatible with 10.7GB headroom. ✅ RTX 4070 Ti (16GB): Yes, fully compatible with 14.3GB headroom. ✅ RTX 4090 (24GB): Yes, fully compatible with 21.5GB headroom
How much VRAM does JH XMP Metadata Nodes take on an RTX 3060 vs RTX 4090?
On an RTX 3060 (12GB VRAM), JH XMP Metadata Nodes runs smoothly on an RTX 3060 (12GB) with 10.7GB of headroom. This is sufficient to run the node alongside standard SD 1.5 and SDXL workflows in full precision. On an RTX 4090 (24GB VRAM), the node runs with extreme headroom on an RTX 4090 (24GB) with 21.5GB of dedicated headroom. This allows you to combine the node with massive models (like FLUX.1 Dev, Schnell, or Hunyuan Video) in full precision (FP16) without any offload flags.
What Python packages are required for JH XMP Metadata Nodes?
To run JH XMP Metadata Nodes, you need to install: lxml==5.3.0 ; python_version >= "3.12" and python_version < "4.0". You can install these using pip or add them to your requirements.txt file.
How can I reduce VRAM usage when running JH XMP Metadata Nodes?
JH XMP Metadata Nodes supports low VRAM mode. To reduce memory usage: (1) Enable --lowvram or --medvram flags in ComfyUI, (2) Reduce batch size to 1, (3) Use fp16 or fp8 precision if supported, (4) Close other GPU applications.
How do I install JH XMP Metadata Nodes in ComfyUI?
To install JH XMP Metadata Nodes: (1) Navigate to your ComfyUI/custom_nodes directory, (2) Clone the repository: git clone https://github.com/ComfyUI-JH/ComfyUI-JH-XMP-Metadata-Nodes, (3) Install dependencies: pip install -r requirements.txt (if present), (4) Restart ComfyUI. Alternatively, use ComfyUI Manager for one-click installation.